Comparable long‐term functional outcomes of subvastus and medial parapatellar approach in total knee arthroplasty: A 10‐year follow‐up study
Bibliographic record
Abstract
Abstract Background Surgeons usually use the medial parapatellar or subvastus approaches for total knee arthroplasty (TKA). The subvastus approach is rapidly gaining recognition to reduce damage to the extensional mechanism and recover faster after surgery. This study compares the long‐term outcomes of the conventional medial parapatellar and subvastus approaches in primary TKA during a minimum 10‐year follow‐up. Methods In a retrospective longitudinal follow‐up study from 2008 to 2013, 60 eligible patients for primary TKA were included. The patients were divided into two groups: one undergoing TKA with the subvastus approach (n = 30) and the other with the conventional medial parapatellar approach (n = 30). Postoperatively, the patients were followed up for 10–15 years. Patients were assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Knee Society Score (KSS), and Visual Analogue Scale index for pain. Results The time required to perform an active straight leg raise (SLR) was significantly shorter in the subvastus group (p < 0.001) at early postoperation evaluation. Patients in the subvastus group had lower pain and better knee functional scores at the one‐year follow‐up (p < 0.05). There was no difference between the two groups regarding duration of hospitalisation, blood loss, operation time, length of the scar, and postoperative complications. Both approaches had similar long‐term results regarding pain and functional scores of WOMAC (6.2 ± 1.2 vs. 6.3 ± 1.3, p‐value = 0.69) and KSS scores (93.1 ± 6.8 vs. 95.0 ± 3.2, p‐value = 0.42). Conclusion The subvastus approach was associated with a shorter time to achieve active SLR, higher functional scores, and better pain relief at early postoperative evaluations. However, both techniques had similar long‐term outcomes in terms of pain and functional scores, as measured by the WOMAC and KSS scales. Level of Evidence II
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".